PFI-TT: Interactive Software for Hyperspectral Image Analysis

PFI-TT：用于高光谱图像分析的交互式软件

• 批准号: 1827656
• 负责人: Mikhail Berezin
• 金额: $ 20万
• 依托单位: Washington University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-08-01 至 2022-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1827656&HistoricalAwards=false
• 关键词: PFI; TT; Interactive; Software; Hyperspectral

The broader impact/commercial potential of this PFI project is to advance, facilitate and streamline the analysis of hyperspectral imaging data. In contrast to two-dimensional images produced by conventional color cameras usually with three-color channels, hyperspectral techniques generate three-dimensional datasets known as datacubes with both spatial and spectral dimensions with hundreds of color channels. Hyperspectral methods lead to a vastly improved ability to classify and differentiate objects based on their spectral features enabling small, otherwise unnoticeable, features to be amplified. The societal impact of hyperspectral imaging is rapidly expanding and includes the development of advanced medical diagnostic tools, systems for precision agriculture, methods to ensure food quality, discovery of new minerals, and the evolution of national defense projects. However, despite the progress in analysis method development and computational speed, efficient computational approaches suitable for rapid analysis of datacubes are still lacking. Three-dimensionality of data and large file size presents a significant challenge for isolation of useful data, and consequently individual investigators developing their own tools. The development of an advanced and commercially available software package for hyperspectral data analysis will facilitate this work and enable users to search for new optical signatures across multiple applications. The proposed project sets a new software architecture that is capable of analyzing complex and computationally challenging datasets recorded by hyperspectral imaging systems. The proposed computational platform for data analysis of hyperspectral data will provide a unique and powerful tool to process hyperspectral data for a variety of applications: from medicine and remote vision to forensics and plants science. The research objective is to integrate current and new analysis methods into an interactive, fast, and intuitive hyperspectral imaging software that will offer an unprecedented level of flexibility in hyperspectral data processing. The proposed software program will be designed to be operated without any prior programming skills allowing interactive sessions of raw and processed data to be easily shared, explored and evaluated by both peers and newcomers. The major feature of the software is an open architecture that provides a common framework for the implementation of the additional methods from the user community. The commercially available platform will have structural flexibility to allow users to integrate their novel computational methods with relatively little effort. With this feature, the software is expected to be a central part of hyperspectral technology and will be the preferred software of the majority of users.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

该PFI项目的更广泛的影响/商业潜力是促进，促进和简化高光谱成像数据的分析。 与通常具有三色通道的常规彩色相机产生的二维图像相反，高光谱技术生成三维数据集，称为数据存储，具有数百个颜色通道的空间和光谱尺寸。高光谱方法可以根据其光谱特征对物体进行分类和分化的能力大大提高，从而可以放大小，否则不明显的特征。高光谱成像的社会影响正在迅速扩展，包括开发先进的医学诊断工具，精确农业的系统，确保食品质量，发现新矿物质的方法以及国防项目的发展。然而，尽管分析方法开发和计算速度取得了进展，但仍缺乏适合于数据存储的快速分析的有效计算方法。数据和大文件大小的三维性为隔离有用数据带来了重大挑战，因此，个人研究人员开发了自己的工具。开发用于高光谱数据分析的高级和市售软件包将有助于这项工作，并使用户能够在多个应用程序中搜索新的光学签名。 拟议的项目设置了一个新的软件体系结构，该架构能够分析高光谱成像系统记录的复杂和计算挑战性的数据集。提议的用于高光谱数据数据分析的计算平台将为各种应用程序处理高光谱数据的独特而有力的工具：从医学和远程视力到取证和植物科学。研究目标是将当前和新的分析方法整合到交互式，快速和直观的高光谱成像软件中，该软件将在高光谱数据处理中提供前所未有的灵活性水平。提出的软件程序将设计为在没有任何事先编程技能的情况下进行操作，允许同行和新手都可以轻松地共享，探索和评估原始数据和处理的数据的交互式会话。该软件的主要功能是开放式体系结构，为实现用户社区的其他方法提供了共同的框架。市售平台将具有结构性的灵活性，使用户能够以相对较少的精力整合其新颖的计算方法。借助此功能，该软件有望成为高光谱技术的核心部分，并将成为大多数用户的首选软件。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子和更广泛影响的评估评估标准来通过评估来获得支持的。

项目成果

IDCube Lite: Free Interactive Discovery Cube software for multi and hyperspectral applications

IDCube Lite：适用于多光谱和高光谱应用的免费交互式 Discovery Cube 软件

• DOI: 10.1255/jsi.2021.a1
• 发表时间: 2021
• 期刊: Journal of Spectral Imaging
• 作者: Mishra, Deependra;Hurbon, Helena;Wang, John;Wang, Steven;Du, Tommy;Wu, Qian;Kim, David;Basir, Shiva;Cao, Qian;Zhang, Hairong
• 通讯作者: Zhang, Hairong

HSKL: A Machine Learning Framework for Hyperspectral Image Analysis

• DOI: 10.1109/whispers52202.2021.9483968
• 发表时间: 2021-03
• 期刊: 2021 11th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS)
• 作者: Qian Cao;Deependra Mishra;John Wang;Steven T. Wang;Helena Hurbon;M. Berezin

Selection of Hyperspectral Endmember Extraction Algorithm for Tumor Delineation in Animal Models

用于动物模型肿瘤勾画的高光谱端元提取算法的选择

• 发表时间: 2021
• 期刊: paper OF2E.2
• 作者: Mishra, D;Wang, J;Wang, ST;Cao, Q;Hurbon, H;Akers, W;Berezin, MY
• 通讯作者: Berezin, MY

Idcube Lite – A Free Interactive Discovery Cube Software for Multi And Hyperspectral Applications

Idcube Lite — 适用于多光谱和高光谱应用的免费交互式 Discovery Cube 软件

• 发表时间: 2021
• 期刊: Workshop on Hyperspectral Image and Signal Processing Evolution in Remote Sensing
• 作者: Mishra, D;Hurbon, H;Wang, J;Wang, T;Kim, D;Cao, Q;Basir, S;Zhang, H
• 通讯作者: Zhang, H